Uncovering dynamic stock return correlations with multilayer network analysis

Abstract

We apply recent innovations in network science to analyze how correlations of stock returns evolve over time. To illustrate these techniques we study the returns of 30 industry stock portfolios from 1927 to 2014. We calculate Pearson correlation matrices for each year, and apply multilayer network tools to these correlation matrices to uncover mesoscale architecture in the form of communities. These communities are easily interpretable as groups of industries with highly correlated stock returns. We observe that the flexibility, or the likelihood of industries to switch communities, exhibits a statistically significant increase after 1970, and that the communities evolve in ways consistent with changes in the structure of the U.S. economy. We find that these patterns are not explained by changes in average pairwise correlations or industry market betas. These results therefore underscore the potential for using multilayer network tools to study time-varying correlations of financial assets.